{"id":"W2027696432","doi":"10.2298/csis0902047m","title":"Model transformations to bridge concrete and abstract syntax of web rule languages","year":2009,"lang":"en","type":"article","venue":"Computer Science and Information Systems","topic":"Semantic Web and Ontologies","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"Athabasca University","funders":"Brandenburgische Technische Universität Cottbus-Senftenberg; European Commission; Natural Sciences and Engineering Research Council of Canada; Athabasca University","keywords":"RuleML; Computer science; Semantic Web Rule Language; Programming language; Abstract syntax; Syntax; Abstract syntax tree; Syntax error; XML; Natural language processing; Metamodeling; XML Schema (W3C); Markup language; Artificial intelligence; XHTML; Web service; Document Structure Description; World Wide Web; Web standards; Document type definition; Semantic analytics","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005958244,0.00008604147,0.0001525891,0.0002703927,0.0001584787,0.0004902058,0.0004479724,0.00003052478,2.638155e-7],"category_scores_gemma":[0.00002409256,0.00007002402,0.00001619466,0.0004001991,0.00009600439,0.005708528,0.00007894516,0.00004253469,0.000009336594],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001613294,"about_ca_system_score_gemma":0.0001123942,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002115498,"about_ca_topic_score_gemma":6.214124e-7,"domain_scores_codex":[0.9990036,0.000008506552,0.000336718,0.0001309293,0.0003435443,0.0001767347],"domain_scores_gemma":[0.999301,0.00003043804,0.0001030698,0.0002313827,0.0002277971,0.0001062942],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00001267151,0.00001875467,0.0005137459,0.0003362901,0.00001632214,0.000002994423,0.03906814,0.01127228,0.00965801,0.3536353,0.003765941,0.5816996],"study_design_scores_gemma":[0.0001789365,0.00008707961,0.01987965,0.00005843754,0.000001874086,0.00003923343,0.000180022,0.9777371,0.0007995284,0.0001049778,0.0008154418,0.0001177735],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3224057,0.0001234225,0.6729469,0.0007322267,0.0001649246,0.0002205465,0.000007302372,0.0000860673,0.003312892],"genre_scores_gemma":[0.9856795,0.0000327404,0.01355416,0.0007064749,0.0000168664,0.000003712721,0.000001085662,7.858202e-7,0.000004637253],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9664648,"threshold_uncertainty_score":0.4727066,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01983014678119117,"score_gpt":0.2589773673314072,"score_spread":0.239147220550216,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}